In this experiment, we focus on a scenario with \(M = 2\)  prompts, “Walt Disney World.” and “daytime picture of Tokyo.”
We compare two models, our proposed approach with Optimal Transport (Equation REF ) and the baseline using Mean (Equation REF ),
with the purpose of investigating the behavior of these methods and the difference made by our approach.
We keep all other configurations the same.
Namely, we use a pre-trained VQGAN [1]} on Imagenet dataset, \(N=64\)  randomly sampled patch, and 1000 iterations of updating \(z\) .
We organize the conducted tasks as explained before.
